Causal Inference in Accounting Research

• 成果类型: Article
• 署名作者: Gow, Ian D.; Larcker, David F.; Reiss, Peter C.
• 署名单位: Harvard University; Stanford University
• 刊物名称: JOURNAL OF ACCOUNTING RESEARCH
• ISSN/ISSBN: 0021-8456
• DOI: 10.1111/1475-679X.12116
• 发表日期: 2016
• 页码: 477-523
• 关键词: political connections; corporate governance; COMPENSATION; INFORMATION; management; earnings; identification; statistics; disclosure; smoking

摘要

This paper examines the approaches accounting researchers adopt to draw causal inferences using observational (or nonexperimental) data. The vast majority of accounting research papers draw causal inferences notwithstanding the well-known difficulties in doing so. While some recent papers seek to use quasi-experimental methods to improve causal inferences, these methods also make strong assumptions that are not always fully appreciated. We believe that accounting research would benefit from more in-depth descriptive research, including a greater focus on the study of causal mechanisms (or causal pathways) and increased emphasis on the structural modeling of the phenomena of interest. We argue these changes offer a practical path forward for rigorous accounting research.